The main problems in drawing causal inferences from epidemiological case-control studies are confounding by unmeasured extraneous factors, selection bias and differential misclassification of exposure1. In genetics the first of these, in the form of population structure, has dominated recent debate2,3,4. Population structure explained part of the significant +11.2% inflation of test statistics we observed in an analysis of 6,322 nonsynonymous SNPs in 816 cases of type 1 diabetes and 877 population-based controls from Great Britain. The remainder of the inflation resulted from differential bias in genotype scoring between case and control DNA samples, which originated from two laboratories, causing false-positive associations. To avoid excluding SNPs and losing valuable information, we extended the genomic control method2,3,4,5 by applying a variable downweighting to each SNP.
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We thank the individuals with T1D and control individuals for their participation; G. Coleman, S. Field, T. Mistry, K. Bourget, S. Clayton, M. Hardy, P. Lauder, M. Maisuria, W. Meadows and S. Wood for preparing DNA samples; D. Strachan, R. Jones, S. Ring and W. McArdle for providing DNA from the 1958 British Birth Cohort collection; and A. Long, N. Naclerio, T. Cormier, K. Tran, C. Bruckner and S. Picton for genotyping and technical assistance. We acknowledge use of DNA from the 1958 British Birth Cohort collection, funded by the Medical Research Council and the Wellcome Trust. We thank the Juvenile Diabetes Research Foundation, the Wellcome Trust, Diabetes UK and the Medical Research Council for financial support. D.G.C. is a Juvenile Diabetes Research Foundation and Wellcome Trust Principal Research Fellow.
M. Faham, M.M., H.B.J., M. Falkowski, P.H. and T.D.W. are currently employed by ParAllele Bioscience.
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